Purpose :
To use deep learning algorithms with and without automated segmentation-based preprocessing to classify individual optical coherence tomography (OCT) tomograms as “pathology present” or “pathology absent” based on expert labelling. Increasingly, deep learning approaches have shown success in automating image analysis of such data. Limitations include access to large labelled data and compute power, yet deep learning is now capable of matching expert clinicians, as demonstrated with diabetic retinopathy fundus photos [Gulshan 2016]. One way to overcome the data limitation is through intelligent preprocessing to help reduce model complexity and better address the bias-variance trade-off inherent in machine learning problems.

Methods :
Macular OCT B-scans (Cirrus, Carl Zeiss Meditec, Inc) were used from 100 unique patients with 20 of those patients exhibiting evident pathology, including age-related macular degeneration (AMD), epiretinal membrane (ERM), and high myopia. Each was manually classified and split into 15,081 negative examples (controls), and 7,284 positive examples (pathology). For preprocessing, automated layer segmentation software (Orion, Voxeleron LLC) was used to crop each B-scan from the ILM to a fixed offset below Bruch’s. The cropped B-scans were then resampled to a uniform size (Fig 1). The patients were randomly split into training (60%), validation (10%), and test (30%) sets. A deep convolutional neural network (CNN) using 8 layers was trained [2 convolutional layers, 2 pooling layers, 1 dropout layer, 2 hidden layers, and a soft-max regression layer]. Another CNN was trained on the same data but without the automated segmentation-based preprocessing.

Results :
The accuracy of the system, as measured on the test set, is 98.8% with a sensitivity of 96.9% and a specificity of 99.8%. Without the automated segmentation-based preprocessing, the accuracy drops to 85.3% with a sensitivity of 78.7% and specificity of 88.6%.(Fig 2).